Machine Learning-Assisted Sustainable Remanufacturing, Reusing and Recycling for Lithium-ion Batteries
This work addresses critical barriers in battery remanufacturing, reusing, and recycling to support global energy transition and carbon neutrality, though it appears incremental in integrating existing methods.
The dissertation tackles data scarcity and heterogeneity in lithium-ion battery lifecycle management by developing a machine learning framework that predicts degradation, assesses residual value, sorts materials with privacy, and unifies diagnostics and prognostics, advancing sustainable battery management.
The sustainable utilization of lithium-ion batteries (LIBs) is crucial to the global energy transition and carbon neutrality, yet data scarcity and heterogeneity remain major barriers across remanufacturing, reusing, and recycling. This dissertation develops a machine learning assisted framework to address these challenges throughout the battery lifecycle. A physics informed quality control model predicts long-term degradation from limited early-cycle data, while a generative learning based residual value assessment method enables rapid and accurate evaluation of retired batteries under random conditions. A federated learning strategy achieves privacy preserving and high precision cathode material sorting, supporting efficient recycling. Furthermore, a unified diagnostics and prognostics framework based on correlation alignment enhances adaptability across tasks such as state of health estimation, state of charge estimation, and remaining useful life prediction under varied testing protocols. Collectively, these contributions advance sustainable battery management by integrating physics, data generation, privacy preserving collaboration, and adaptive learning, offering methodological innovations to promote circular economy and global carbon neutrality.